Background: In Brazil, a substantial number of coronavirus disease (COVID-19) cases and deaths have been reported. It has become the second most affected country worldwide, as of June 9, 2020. Official Brazilian government sources present contradictory data on the impact of the disease; thus, it is possible that the actual number of infected individuals and deaths in Brazil is far larger than those officially reported. It is very likely that the actual spread of the disease has been underestimated. Objective: This study investigates the underreporting of cases and deaths related to COVID-19 in the most affected cities in Brazil, based on public data available from official Brazilian government internet portals, to identify the actual impact of the pandemic. Methods: We used data from historical deaths due to respiratory problems and other natural causes from two public portals: DATASUS (Department of Informatics of the Unified Healthcare System) (2010-2018) and the Brazilian Transparency Portal of Civil Registry (2019-2020). These data were used to build time-series models (modular regressions) to predict the expected mortality patterns for 2020. The forecasts were used to estimate the possible number of deaths that were incorrectly registered during the pandemic and posted on government internet portals in the most affected cities in the country. Results: Our model found a significant difference between the real and expected values. The number of deaths due to severe acute respiratory syndrome (SARS) was considerably higher in all cities, with increases between 493% and 5820%. This sudden increase may be associated with errors in reporting. An average underreporting of 40.68% (range 25.9%-62.7%) is estimated for COVID-19–related deaths. Conclusions: The significant rates of underreporting of deaths analyzed in our study demonstrate that officially released numbers are much lower than actual numbers, making it impossible for the authorities to implement a more effective pandemic response. Based on analyses carried out using different fatality rates, it can be inferred that Brazil’s epidemic is worsening, and the actual number of infectees could already be between 1 to 5.4 million.

Resilience is the ability of a system to adapt, persist, and transform as a reaction to threats, which may be external or internal to the system, while vulnerability is the state of being susceptible to harm from exposure to stresses associated with environmental and social change and from the inability to adapt. Based on a study of the threats that can affect urban mobility, we identified a gap regarding the analysis of the levels of resilience and vulnerability in the face of subsidy threats that can severely affect developing countries. This article measures the level of resilience and vulnerability due to the absence of public transport fare subsidies. For this purpose, we developed an approach based on fuzzy logic and applied it in 33 administrative regions (ARs) of the city of Rio de Janeiro, Brazil. We obtained four matrices of the levels of vulnerability and resilience of each of the regions as an origin and destination. The results show that areas nearest to the downtown region and those with high-capacity transportation available (commuter train and/or subway, systems with many transfer points) are more resilient, while a high level of vulnerability is associated with low income, negative socioeconomic indicators, and the predominance of road transportation to reach jobs. The contribution of this paper is the method applied to analyse the levels of vulnerability and resilience of public transport, which includes a threat that can cause a rupture that impacts routines and job accessibility in a region.

Traffic demand in wireless communication systems has emerged as a key issue over recent decades. An ever-increasing trend is projected for the next few years, with explosive data traffic expected to materialize and mobile users imposing new quality of service requirements. This growing traffic demand, combined with increasingly complex heterogeneous network (HetNet) scenarios, has presented ever more challenges for mobile network operators in terms of service, coverage, load balancing, and quality of service. Considering the traditional association mechanism based on maximum power received, HetNets tend to remain unbalanced, making it challenging to satisfy mobile users’ traffic requirements. In this paper, instead of trying to maximize the achievable downlink rate per user, we couple the cell range expansion (CRE) technique with a particle swarm optimization (PSO) algorithm to maximize the number of users whose downlink requirements are met. The proposed scheme considers both the loads of base stations and the signal-to-interference-plus-noise ratio (SINR) of user equipment to model an objective function that seeks to compute specific CRE bias values per small base station. The proposed scheme is also compared with some classical PSO implementations. Numerical results validate the performance of the proposed schemes, which effectively fulfill users’ data traffic requirements by reducing network imbalance.

This paper presents an estimation of Quality of Experience (QoE) metrics based on Quality of Service (QoS) metrics in WiMAX networks. Applications used to generate such estimations were EvalVid and Network Simulator 2 (NS-2). The QoE was estimated by employing a Multilayer Artificial Neural Network by means of the WEKA tool. The results show a very efficient estimation of metrics of QoE parameters with respect to QoS parameters.

The world faces a pandemic not previously experienced in modern times. The internal mechanism of SARS-Cov-2 is not well known and there are no Pharmaceutical Interventions available. To stem the spread of the virus, measures of respiratory etiquette, social distancing and hand hygiene have been recommended. Based on these measures, some countries have already managed to control the COVID-19 propagation, although in the absence of pharmaceutical interventions, this control is not definitive. However, we have seen that social heterogeneity across populations makes the effects of COVID-19 also different. Social inequality affects the population of developing countries not only from an economic point of view. The relationship between social inequality and the health condition is not new, but it becomes even more evident in times of crisis, such as the one the world has been facing with COVID-19. How does social inequality affect the COVID-19 propagation in developing countries is the object of this study. We propose a new epidemic SEIR model based on social indicators to predict outbreak and mortality of COVID-19. The estimated number of infected and fatalities are compared with different levels of Non-Pharmaceutical Interventions. We present a case study for the Deep Brazil. The results showed that social inequality has a strong effect on the propagation of COVID-19, increasing its damage and accelerating the collapse of health infrastructure.